##Introduction
There has tremendous growth in the area undergraduate biology research over the last two decades. This study attempts to summarize and analyze the progress of this growth.
This is a placeholder for the inital data set Whole group
txt <- c("rawdata/CBE_LSE_2010-2019/20200717_CBE2010-2019-WOS-1-500.txt",
"rawdata/CBE_LSE_2010-2019/20200717_CBE2010-2019-WOS-501-708.txt")
data <- convert2df(file = txt, dbsource = 'wos', format = "plaintext")
##
## Converting your wos collection into a bibliographic dataframe
##
##
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
##
## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://cran.r-project.org/web/packages/bibliometrix/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://cran.r-project.org/web/packages/bibliometrix/vignettes/bibliometrix-vignette.html)
##
##
## Missing fields: DEDone!
##
##
## Generating affiliation field tag AU_UN from C1: Done!
results <- biblioAnalysis(data, sep = ";")
options(width=100)
S <- summary(object = results, k = 10, pause = FALSE)
##
##
## MAIN INFORMATION ABOUT DATA
##
## Timespan 2010 : 2019
## Sources (Journals, Books, etc) 1
## Documents 708
## Average years from publication 4.97
## Average citations per documents 14.12
## Average citations per year per doc 2.044
## References 18276
##
## DOCUMENT TYPES
## article 656
## correction 18
## letter 30
## review 4
##
## DOCUMENT CONTENTS
## Keywords Plus (ID) 983
## Author's Keywords (DE) 0
##
## AUTHORS
## Authors 2295
## Author Appearances 3255
## Authors of single-authored documents 59
## Authors of multi-authored documents 2236
##
## AUTHORS COLLABORATION
## Single-authored documents 70
## Documents per Author 0.308
## Authors per Document 3.24
## Co-Authors per Documents 4.6
## Collaboration Index 3.5
##
##
## Annual Scientific Production
##
## Year Articles
## 2010 59
## 2011 40
## 2012 50
## 2013 76
## 2014 71
## 2015 58
## 2016 105
## 2017 84
## 2018 84
## 2019 81
##
## Annual Percentage Growth Rate 3.583971
##
##
## Most Productive Authors
##
## Authors Articles Authors Articles Fractionalized
## 1 TANNER KD 23 TANNER KD 10.81
## 2 BROWNELL SE 21 BROWNELL SE 5.92
## 3 KNIGHT JK 21 KNIGHT JK 5.59
## 4 SMITH MK 17 [ANONYMOUS] 5.00
## 5 DOLAN EL 16 ALLEN D 4.14
## 6 ANDREWS TC 11 BRAME CJ 3.83
## 7 COUCH BA 11 SMITH MK 3.78
## 8 CORWIN LA 10 DOLAN EL 3.60
## 9 EDDY SL 10 ANDREWS TC 3.14
## 10 WENDEROTH MP 10 SCHUSSLER EE 2.68
##
##
## Top manuscripts per citations
##
## Paper TC TCperYear
## 1 AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC 216 30.9
## 2 JENSEN JL, 2015, CBE-LIFE SCI EDUC 215 35.8
## 3 BROWNELL SE, 2012, CBE-LIFE SCI EDUC 163 18.1
## 4 ANDREWS TM, 2011, CBE-LIFE SCI EDUC 154 15.4
## 5 EDDY SL, 2014, CBE-LIFE SCI EDUC 134 19.1
## 6 MORAVEC M, 2010, CBE-LIFE SCI EDUC 130 11.8
## 7 MAHER JM, 2013, CBE-LIFE SCI EDUC 127 15.9
## 8 TANNER KD, 2012, CBE-LIFE SCI EDUC 125 13.9
## 9 SMITH MK, 2013, CBE-LIFE SCI EDUC 117 14.6
## 10 SMITH MK, 2011, CBE-LIFE SCI EDUC-a 112 11.2
##
##
## Corresponding Author's Countries
##
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 638 0.92464 615 23 0.0361
## 2 CANADA 16 0.02319 13 3 0.1875
## 3 AUSTRALIA 4 0.00580 4 0 0.0000
## 4 NETHERLANDS 4 0.00580 4 0 0.0000
## 5 SWEDEN 4 0.00580 2 2 0.5000
## 6 GERMANY 3 0.00435 2 1 0.3333
## 7 ISRAEL 2 0.00290 2 0 0.0000
## 8 NEW ZEALAND 2 0.00290 1 1 0.5000
## 9 NORWAY 2 0.00290 2 0 0.0000
## 10 UNITED KINGDOM 2 0.00290 2 0 0.0000
##
##
## SCP: Single Country Publications
##
## MCP: Multiple Country Publications
##
##
## Total Citations per Country
##
## Country Total Citations Average Article Citations
## 1 USA 9534 14.94
## 2 CANADA 127 7.94
## 3 NETHERLANDS 79 19.75
## 4 SWEDEN 78 19.50
## 5 AUSTRALIA 60 15.00
## 6 ISRAEL 18 9.00
## 7 SLOVENIA 14 14.00
## 8 CHINA 13 13.00
## 9 CZECH REPUBLIC 13 13.00
## 10 FRANCE 13 13.00
##
##
## Most Relevant Sources
##
## Sources Articles
## 1 CBE-LIFE SCIENCES EDUCATION 708
S
## $MainInformation
## [1] "\n\nMAIN INFORMATION ABOUT DATA\n\n" "Timespan 2010 : 2019 \n"
## [3] "Sources (Journals, Books, etc) 1 \n" "Documents 708 \n"
## [5] "Average years from publication 4.97 \n" "Average citations per documents 14.12 \n"
## [7] "Average citations per year per doc 2.044 \n" "References 18276 \n"
## [9] "\nDOCUMENT TYPES \n" "article 656 \n"
## [11] "correction 18 \n" "letter 30 \n"
## [13] "review 4 \n" "\nDOCUMENT CONTENTS\n"
## [15] "Keywords Plus (ID) 983 \n" "Author's Keywords (DE) 0 \n"
## [17] "\nAUTHORS\n" "Authors 2295 \n"
## [19] "Author Appearances 3255 \n" "Authors of single-authored documents 59 \n"
## [21] "Authors of multi-authored documents 2236 \n" "\nAUTHORS COLLABORATION\n"
## [23] "Single-authored documents 70 \n" "Documents per Author 0.308 \n"
## [25] "Authors per Document 3.24 \n" "Co-Authors per Documents 4.6 \n"
## [27] "Collaboration Index 3.5 \n" "\n"
##
## $MainInformationDF
## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2010:2019
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 708
## 5 Average years from publication 4.97
## 6 Average citations per documents 14.12
## 7 Average citations per year per doc 2.044
## 8 References 18276
## 9 DOCUMENT TYPES
## 10 article 656
## 11 correction 18
## 12 letter 30
## 13 review 4
## 14 DOCUMENT CONTENTS
## 15 Keywords Plus (ID) 983
## 16 Author's Keywords (DE) 0
## 17 AUTHORS
## 18 Authors 2295
## 19 Author Appearances 3255
## 20 Authors of single-authored documents 59
## 21 Authors of multi-authored documents 2236
## 22 AUTHORS COLLABORATION
## 23 Single-authored documents 70
## 24 Documents per Author 0.308
## 25 Authors per Document 3.24
## 26 Co-Authors per Documents 4.6
## 27 Collaboration Index 3.5
## 28
##
## $AnnualProduction
## Year Articles
## 1 2010 59
## 2 2011 40
## 3 2012 50
## 4 2013 76
## 5 2014 71
## 6 2015 58
## 7 2016 105
## 8 2017 84
## 9 2018 84
## 10 2019 81
##
## $AnnualGrowthRate
## [1] 3.583971
##
## $MostProdAuthors
## Authors Articles Authors Articles Fractionalized
## 1 TANNER KD 23 TANNER KD 10.81
## 2 BROWNELL SE 21 BROWNELL SE 5.92
## 3 KNIGHT JK 21 KNIGHT JK 5.59
## 4 SMITH MK 17 [ANONYMOUS] 5.00
## 5 DOLAN EL 16 ALLEN D 4.14
## 6 ANDREWS TC 11 BRAME CJ 3.83
## 7 COUCH BA 11 SMITH MK 3.78
## 8 CORWIN LA 10 DOLAN EL 3.60
## 9 EDDY SL 10 ANDREWS TC 3.14
## 10 WENDEROTH MP 10 SCHUSSLER EE 2.68
##
## $MostCitedPapers
## Paper TC TCperYear
## 1 AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC 216 30.9
## 2 JENSEN JL, 2015, CBE-LIFE SCI EDUC 215 35.8
## 3 BROWNELL SE, 2012, CBE-LIFE SCI EDUC 163 18.1
## 4 ANDREWS TM, 2011, CBE-LIFE SCI EDUC 154 15.4
## 5 EDDY SL, 2014, CBE-LIFE SCI EDUC 134 19.1
## 6 MORAVEC M, 2010, CBE-LIFE SCI EDUC 130 11.8
## 7 MAHER JM, 2013, CBE-LIFE SCI EDUC 127 15.9
## 8 TANNER KD, 2012, CBE-LIFE SCI EDUC 125 13.9
## 9 SMITH MK, 2013, CBE-LIFE SCI EDUC 117 14.6
## 10 SMITH MK, 2011, CBE-LIFE SCI EDUC-a 112 11.2
##
## $MostProdCountries
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 638 0.92464 615 23 0.0361
## 2 CANADA 16 0.02319 13 3 0.1875
## 3 AUSTRALIA 4 0.00580 4 0 0.0000
## 4 NETHERLANDS 4 0.00580 4 0 0.0000
## 5 SWEDEN 4 0.00580 2 2 0.5000
## 6 GERMANY 3 0.00435 2 1 0.3333
## 7 ISRAEL 2 0.00290 2 0 0.0000
## 8 NEW ZEALAND 2 0.00290 1 1 0.5000
## 9 NORWAY 2 0.00290 2 0 0.0000
## 10 UNITED KINGDOM 2 0.00290 2 0 0.0000
##
## $TCperCountries
## Country Total Citations Average Article Citations
## 1 USA 9534 14.94
## 2 CANADA 127 7.94
## 3 NETHERLANDS 79 19.75
## 4 SWEDEN 78 19.50
## 5 AUSTRALIA 60 15.00
## 6 ISRAEL 18 9.00
## 7 SLOVENIA 14 14.00
## 8 CHINA 13 13.00
## 9 CZECH REPUBLIC 13 13.00
## 10 FRANCE 13 13.00
##
## $MostRelSources
## Sources Articles
## 1 CBE-LIFE SCIENCES EDUCATION 708
##
## $MostRelKeywords
## NULL
plot(x=results, k=10, pause=F)
## Warning: Use of `xx$Country` is discouraged. Use `Country` instead.
## Warning: Use of `xx$Freq` is discouraged. Use `Freq` instead.
## Warning: Use of `xx$Collaboration` is discouraged. Use `Collaboration` instead.
## Warning: Use of `Y$Year` is discouraged. Use `Year` instead.
## Warning: Use of `Y$Freq` is discouraged. Use `Freq` instead.
## Warning: Use of `Y$Year` is discouraged. Use `Year` instead.
## Warning: Use of `Y$Freq` is discouraged. Use `Freq` instead.
## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.
## Warning: Use of `Table2$MeanTCperYear` is discouraged. Use `MeanTCperYear` instead.
## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.
## Warning: Use of `Table2$MeanTCperYear` is discouraged. Use `MeanTCperYear` instead.
## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.
## Warning: Use of `Table2$MeanTCperArt` is discouraged. Use `MeanTCperArt` instead.
## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.
## Warning: Use of `Table2$MeanTCperArt` is discouraged. Use `MeanTCperArt` instead.
S$MainInformationDF
## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2010:2019
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 708
## 5 Average years from publication 4.97
## 6 Average citations per documents 14.12
## 7 Average citations per year per doc 2.044
## 8 References 18276
## 9 DOCUMENT TYPES
## 10 article 656
## 11 correction 18
## 12 letter 30
## 13 review 4
## 14 DOCUMENT CONTENTS
## 15 Keywords Plus (ID) 983
## 16 Author's Keywords (DE) 0
## 17 AUTHORS
## 18 Authors 2295
## 19 Author Appearances 3255
## 20 Authors of single-authored documents 59
## 21 Authors of multi-authored documents 2236
## 22 AUTHORS COLLABORATION
## 23 Single-authored documents 70
## 24 Documents per Author 0.308
## 25 Authors per Document 3.24
## 26 Co-Authors per Documents 4.6
## 27 Collaboration Index 3.5
## 28
write.csv(file = "keywords.csv",as.data.frame(results$ID[1:20]))
write.csv(file = "Maininformation.csv",S$MainInformationDF)
write.csv(file = "ArticlesYears.csv",S$AnnualProduction)
write.csv(file = "MostCitedPapers.csv",S$MostCitedPapers)
write.csv(file = "MostProdAuthors.csv", S$MostProdAuthors)
##Authoring
authors=gsub(","," ",names(results$Authors)[1:10])
indices <- Hindex(data, field = "author", elements=authors, sep = ";", years = 50)
indices$H
## Author h_index g_index m_index TC NP PY_start
## 1 TANNER KD 11 23 1.0000000 667 23 2010
## 2 BROWNELL SE 10 21 1.1111111 566 21 2012
## 3 KNIGHT JK 9 20 0.8181818 416 21 2010
## 4 SMITH MK 9 17 0.8181818 424 17 2010
## 5 DOLAN EL 11 16 1.0000000 604 16 2010
## 6 ANDREWS TC 5 10 0.7142857 115 11 2014
## 7 COUCH BA 6 10 1.0000000 110 11 2015
## 8 CORWIN LA 5 10 0.8333333 170 10 2015
## 9 EDDY SL 7 10 1.0000000 373 10 2014
## 10 WENDEROTH MP 7 10 0.6363636 318 10 2010
avgAU <- as.data.frame(cbind(results[["nAUperPaper"]],results[["Years"]]))
colnames(avgAU) <-c("nAu","Years")
avgAUres<-avgAU %>%
mutate(Years = as.factor(Years)) %>%
group_by(Years) %>%
summarize(avg = mean(nAu), med = median(nAu))
## `summarise()` ungrouping output (override with `.groups` argument)
lm(nAu ~ Years, avgAU %>% mutate(Years = Years - 2010))
##
## Call:
## lm(formula = nAu ~ Years, data = avgAU %>% mutate(Years = Years -
## 2010))
##
## Coefficients:
## (Intercept) Years
## 3.8091 0.1567
ggplot(avgAU,aes(Years,nAu)) +
geom_point() +
geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(avgAUres,aes(x = Years, y = med)) +
geom_point() +
geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Germaine Ng
This is the placeholder for the question about collaborations between different universities (institutions) Text to introduce the script and outputs
Affiliation=results$Affiliations[1:20]
Affiliation
## AFF
## MICHIGAN STATE UNIV UNIV COLORADO UNIV GEORGIA UNIV WASHINGTON
## 83 67 65 58
## UNIV WISCONSIN SAN FRANCISCO STATE UNIV PURDUE UNIV UNIV CALIF LOS ANGELES
## 53 44 37 37
## WASHINGTON UNIV ARIZONA STATE UNIV UNIV BRITISH COLUMBIA DUKE UNIV
## 35 33 30 29
## UNIV NEBRASKA EMORY UNIV UNIV TEXAS AUSTIN UNIV CALIF SAN DIEGO
## 26 24 24 23
## UNIV MARYLAND UNIV MINNESOTA VANDERBILT UNIV YALE UNIV
## 23 23 23 23
Aff_Freq=results[["Aff_frac"]] %>%
arrange(desc(Frequency))%>%
top_n(10)
## Selecting by Frequency
Aff_Freq
## Affiliation Frequency
## 1 MICHIGAN STATE UNIV 24.805700
## 2 UNIV COLORADO 23.977510
## 3 UNIV GEORGIA 22.598521
## 4 UNIV WASHINGTON 21.628175
## 5 SAN FRANCISCO STATE UNIV 17.965443
## 6 ARIZONA STATE UNIV 14.793254
## 7 UNIV WISCONSIN 14.505231
## 8 PURDUE UNIV 10.394051
## 9 UNIV BRITISH COLUMBIA 10.083333
## 10 UNIV MINNESOTA 9.596825
## Warning in closeness(bsk.network): At centrality.c:2784 :closeness centrality is not well-defined for disconnected
## graphs
## san francisco state univ washington univ univ colorado grinnell coll
## 342 266 222 219
## univ calif san diego duke univ
## 214 210
## Warning in closeness(bsk.network): At centrality.c:2784 :closeness centrality is not well-defined for disconnected
## graphs
Darcie Nelson
library(stringdist)
##
## Attaching package: 'stringdist'
## The following object is masked from 'package:tidyr':
##
## extract
library(stringr)
library(stringi)
library(plyr)
## ----------------------------------------------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ----------------------------------------------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
## The following object is masked from 'package:purrr':
##
## compact
library(dplyr)
library(magrittr)
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:stringdist':
##
## extract
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
library(broom)
library(lazyeval)
##
## Attaching package: 'lazyeval'
## The following objects are masked from 'package:purrr':
##
## is_atomic, is_formula
library(tidyr)
library(reshape2)
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
##
## smiths
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:reshape2':
##
## dcast, melt
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following object is masked from 'package:purrr':
##
## transpose
#citations
# load functions
source("cleaningfunctions/citation_functions.R")
# Extract citations from WOS list
work_data <- as.data.frame(extract_citation(data, "CR"))
## Warning in melt(citations): The melt generic in data.table has been passed a list and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well.
## To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the
## namespace like reshape2::melt(citations). In the next version, this warning will become an error.
#work_data$label <- as.character(work_data$label)
work_data$L1<- as.factor(work_data$L1)
work_data$index<-as.factor( work_data$index)
# Standardize the data--------------------------------------------------------------------
std_data <- standardize(work_data)
# show authors who are longer than 50 characters
author_temp <- std_data %>%
select(authors) %>%
filter(nchar(authors) > 50)
# clean and standardize author names -----------------------------------------------------
# Run fuzzy match function on a .2 threshold
std_data$authorBlock <- fuzzy_match(std_data$authors, .1, std_data$authors)
# look at author clusters
author_merges <- std_data %>%
group_by(authorBlock) %>%
dplyr::summarise(count = n()) %>%
filter(count > 15) %>%
arrange(desc(count))
## `summarise()` ungrouping output (override with `.groups` argument)
# Run the clustering algorithm again to recluster top merge
# get the top merged author
name <- author_merges[1, ][[1]]
std_data2 <- std_data %>%
filter(authorBlock == name) %>%
mutate(authorBlock = fuzzy_match(authors, .08, authors))
std_data3 <- std_data %>%
filter(authorBlock != name) %>%
bind_rows(std_data2)
std_data4 <- std_data3 %>%
filter(authorBlock == name) %>%
mutate(authorBlock = fuzzy_match(authors, .05, authors))
std_data5 <- std_data3 %>%
filter(authorBlock != name) %>%
bind_rows(std_data4)
#Add specific criteria for problematic author names
std_data5$authorBlock[grep("assaraf", std_data5$authorBlock)]
## [1] "assaraf obz" "assaraf obz" "assaraf obz" "assaraf obz"
# Run clustering algorithm----------------------------------------------------------------
books <- std_data5 %>%
group_by(authorBlock, documentName, documentYear) %>%
filter(type == "book") %>%
mutate(mergedLabel = fuzzy_match(cleanLabel, .05, cleanLabel))
articles <- std_data5 %>%
group_by(authorBlock, documentName, documentYear) %>%
filter(type == "article") %>%
mutate(mergedLabel = fuzzy_match(cleanLabel, .01, cleanLabel))
clean_data <- rbind(books, articles)
# create the flat data file
flat_data<- as.data.table(clean_data)[, toString(paste0(mergedLabel,collapse = ";")), by = list(L1)]
# make sure to unwrap this again to check if it was wrapped properly
rownames(flat_data) <- as.character(flat_data$L1)
flat_data2 <- inner_join(data, flat_data, by = c("SR" = "L1"))
flat_data2$CR <- flat_data2$V1
# find the similarity of the merges------------------------------------------------------
clean_data <- clean_data %>%
mutate(jwSimilarity = calc_jw(cleanLabel, mergedLabel))
# plot------------------------------------------------------------------------------------
merged <- clean_data %>%
filter(jwSimilarity < 1)
hist(round(merged$jwSimilarity, 3),
xlab = "jw similarity distance",
main = "Freq Merges by similarity distance \n with year and document type boundary")
# save the data------------------------------------------------------------------------------------
write.csv(clean_data, "WOS_nodeList.csv", row.names = FALSE)
write.csv(flat_data2, "WOS_clean.csv", row.names = FALSE)
CR <- citations(data, field = "article", sep = ";")
CRtable <-cbind(CR$Cited[1:20])
CRtable
## [,1]
## AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, 2011, VIS CHANG UND BIOL E 159
## FREEMAN S, 2014, P NATL ACAD SCI USA, V111, P8410, DOI 10.1073/PNAS.1319030111 105
## [ANONYMOUS], 2012, ENG EXC PROD ON MILL 75
## CROWE A, 2008, CBE-LIFE SCI EDUC, V7, P368, DOI 10.1187/CBE.08-05-0024 70
## HANDELSMAN J, 2004, SCIENCE, V304, P521, DOI 10.1126/SCIENCE.1096022 62
## SMITH MK, 2008, CBE-LIFE SCI EDUC, V7, P422, DOI 10.1187/CBE.08-08-0045 62
## HAAK DC, 2011, SCIENCE, V332, P1213, DOI 10.1126/SCIENCE.1204820 54
## SEYMOUR E., 1997, TALKING LEAVING WHY 51
## FREEMAN SCOTT, 2007, CBE LIFE SCI EDUC, V6, P132, DOI 10.1187/CBE.06-09-0194 50
## EBERT-MAY D, 2011, BIOSCIENCE, V61, P550, DOI 10.1525/BIO.2011.61.7.9 49
## EDDY SL, 2014, CBE-LIFE SCI EDUC, V13, P453, DOI 10.1187/CBE.14-03-0050 49
## HAKE RR, 1998, AM J PHYS, V66, P64, DOI 10.1119/1.18809 49
## KNIGHT JENNIFER K, 2005, CELL BIOL EDUC, V4, P298, DOI 10.1187/05-06-0082 49
## SEYMOUR E, 2004, SCI EDUC, V88, P493, DOI 10.1002/SCE.10131 48
## HENDERSON C, 2011, J RES SCI TEACH, V48, P952, DOI 10.1002/TEA.20439 47
## RUSSELL SH, 2007, SCIENCE, V316, P548, DOI 10.1126/SCIENCE.1140384 47
## ANDERSON DL, 2002, J RES SCI TEACH, V39, P952, DOI 10.1002/TEA.10053 44
## LOPATTO DAVID, 2007, CBE LIFE SCI EDUC, V6, P297, DOI 10.1187/CBE.07-06-0039 44
## NATIONAL RESEARCH COUNCIL, 2003, BIO2010 TRANSF UND E 44
## AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC, V13, P29, DOI 10.1187/CBE.14-01-0004 43
CR2 <- citations(flat_data2, field = "article", sep = ";")
CR2table <-cbind(CR2$Cited[1:20])
CR2table
## [,1]
## american association for the advancement of science, 2011, vis chang und biol e 185
## freeman s, 2014, p natl acad sci usa, v111, p8410 105
## anonymous, 2012, eng exc prod on mill 75
## crowe a, 2008, cbelife sci educ, v7, p368 70
## handelsman j, 2004, science, v304, p521 62
## smith mk, 2008, cbelife sci educ, v7, p422 62
## seymour e, 1997, talking leaving why 59
## haak dc, 2011, science, v332, p1213 54
## freeman scott, 2007, cbe life sci educ, v6, p132 50
## ebertmay d, 2011, bioscience, v61, p550 49
## eddy sl, 2014, cbelife sci educ, v13, p453 49
## hake rr, 1998, am j phys, v66, p64 49
## handelsman j, 2007, sci teaching 49
## knight jennifer k, 2005, cell biol educ, v4, p298 49
## seymour e, 2004, sci educ, v88, p493 48
## henderson c, 2011, j res sci teach, v48, p952 47
## national research council, 2003, bio2010 transf und e 47
## russell sh, 2007, science, v316, p548 47
## anderson dl, 2002, j res sci teach, v39, p952 44
## lopatto david, 2007, cbe life sci educ, v6, p297 44
write.csv(file = "Citations.csv",CR2table)
sourcestable <-cbind(summary(factor(CR$Source))
[1:10])
sourcestable
## [,1]
## CBE-LIFE SCI EDUC 561
## J RES SCI TEACH 393
## SCIENCE 263
## J COLL SCI TEACH 235
## INT J SCI EDUC 205
## SCI EDUC 203
## J CHEM EDUC 188
## J EDUC PSYCHOL 159
## AM BIOL TEACH 149
## BIOCHEM MOL BIOL EDU 110
write.csv(file = "Sources.csv",sourcestable)
## Warning in closeness(bsk.network): At centrality.c:2784 :closeness centrality is not well-defined for disconnected
## graphs
CS <- conceptualStructure(data,field ="ID", method = "MCA", minDegree=19, clust= 4 ,k.max=20, stemming=TRUE, labelsize=10, documents=10)
## Warning: Use of `A$dim1` is discouraged. Use `dim1` instead.
## Warning: Use of `A$dim2` is discouraged. Use `dim2` instead.
## Warning: Use of `A$nomi` is discouraged. Use `nomi` instead.
## Warning: Use of `A$dim1` is discouraged. Use `dim1` instead.
## Warning: Use of `A$dim2` is discouraged. Use `dim2` instead.
## Warning: Use of `A$nomi` is discouraged. Use `nomi` instead.
## Warning: Use of `B$dim1` is discouraged. Use `dim1` instead.
## Warning: Use of `B$dim2` is discouraged. Use `dim2` instead.
## Warning: Use of `B$nomi` is discouraged. Use `nomi` instead.
## Warning: Use of `B$dim1` is discouraged. Use `dim1` instead.
## Warning: Use of `B$dim2` is discouraged. Use `dim2` instead.
## Warning: Use of `B$nomi` is discouraged. Use `nomi` instead.
#CS4 <- conceptualStructure(data,field ="ID", method = "MCA", minDegree=6, clust= "auto" ,k.max=20, stemming=TRUE, labelsize=4, documents=10)
ggsave("ConceptualStructure.png", plot = CS$graph_terms, device = "png")
## Saving 7 x 5 in image
Social Network Analysis (SNA) of co-authorship: What social structures are organizing co-authorship?
Rachel Kudlacz This is the placeholder for the question about the social struction of co-authorship the among the CBE-LSE authors. A social network analysis approach was used… Text to introduce the script and outputs
Text to explain what is shown in the figures.